title: Efficient calculation of compound similarity based on maximum common subgraphs and its application to prediction of gene transcript levels.
abstract: Properties of a chemical entity, both physical and biological, are related to its structure. Since compound similarity can be used to infer properties of novel compounds, in chemoinformatics much attention has been paid to ways of calculating structural similarity. A useful metric to capture the structural similarity between compounds is the relative size of the Maximum Common Subgraph (MCS). The MCS is the largest substructure present in a pair of compounds, when represented as graphs. However, in practice it is difficult to employ such a metric, since calculation of the MCS becomes computationally intractable when it is large. We propose a novel algorithm that significantly reduces computation time for finding large MCSs, compared to a number of state-of-the-art approaches. The use of this algorithm is demonstrated in an application predicting the transcriptional response of breast cancer cell lines to different drug-like compounds, at a scale which is challenging for the most efficient MCS-algorithms to date. In this application 714 compounds were compared.
authors.affiliation: The Delft Bioinformatics Lab/Kluyver Centre for Genomics of Industrial Fermentation, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Mekelweg 4, 2628 CD Delft, The Netherlands. r.j.p.vanberlo@tudelft.nl
authors.forename: Rogier J P Van
authors.initials: RJ
authors.lastname: Berlo
authors.forename: Wynand
authors.initials: W
authors.lastname: Winterbach
authors.forename: Marco J L De
authors.initials: MJ
authors.lastname: Groot
authors.forename: Andreas
authors.initials: A
authors.lastname: Bender
authors.forename: Peter J T
authors.initials: PJ
authors.lastname: Verheijen
authors.forename: Marcel J T
authors.initials: MJ
authors.lastname: Reinders
authors.forename: Dick De
authors.initials: DD
authors.lastname: Ridder
mesh_terms: D000465:Algorithms; D016208:Databases, Factual; D015195:Drug Design; D008958:Models, Molecular; D004364:Pharmaceutical Preparations / Q000737:chemistry*; D014158:Transcription, Genetic
chemical_list: D004364:Pharmaceutical Preparations